


import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()







def 							lowerCAmelCase_	(   __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase      ):
							return params[F'{prefix}/{prefix}/relpos_bias/rel_embedding'][:, i, :]







def 							lowerCAmelCase_	(   __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase="attention"      ):
							__snake_case						:     Tuple									=  np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/key/kernel'][:, i, :, :]      )
							__snake_case						:     Dict									=  k_tmp.reshape(k_tmp.shape[0]				,      k_tmp.shape[1] * k_tmp.shape[2]      )
							__snake_case						:     str									=  np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/out/kernel'][:, i, :, :]      )
							__snake_case						:     List[str]									=  o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]				,      o_tmp.shape[2]      )
							__snake_case						:     Any									=  np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/query/kernel'][:, i, :, :]      )
							__snake_case						:     Optional[int]									=  q_tmp.reshape(q_tmp.shape[0]				,      q_tmp.shape[1] * q_tmp.shape[2]      )
							__snake_case						:     List[str]									=  np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/value/kernel'][:, i, :, :]      )
							__snake_case						:     List[Any]									=  v_tmp.reshape(v_tmp.shape[0]				,      v_tmp.shape[1] * v_tmp.shape[2]      )
							return k, o, q, v







def 							lowerCAmelCase_	(   __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase=False      ):
							if split_mlp_wi:
														__snake_case						:     Any									=  params[F'{prefix}/{prefix}/mlp/wi_0/kernel'][:, i, :]
														__snake_case						:     List[Any]									=  params[F'{prefix}/{prefix}/mlp/wi_1/kernel'][:, i, :]
														__snake_case						:     Optional[Any]									=  (wi_a, wi_a)
							else:
														__snake_case						:     Optional[int]									=  params[F'{prefix}/{prefix}/mlp/wi/kernel'][:, i, :]

							__snake_case						:     List[Any]									=  params[F'{prefix}/{prefix}/mlp/wo/kernel'][:, i, :]
							return wi, wo







def 							lowerCAmelCase_	(   __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase      ):
							return params[F'{prefix}/{prefix}/{layer_name}/scale'][:, i]







def 							lowerCAmelCase_	(   __lowerCamelCase				,      *, __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase = False      ):
							__snake_case						:     Union[str, Any]									=  traverse_util.flatten_dict(variables["target"]      )
							__snake_case						:     str									=  {"/".join(__lowerCamelCase      ): v for k, v in old.items()}

							# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
							__snake_case						:     Any									=  "encoder/encoder/mlp/wi_0/kernel" in old
							print("Split MLP:"				,      __lowerCamelCase      )

							__snake_case						:     Tuple									=  collections.OrderedDict()

							# Shared embeddings.
							__snake_case						:     Optional[int]									=  old["token_embedder/embedding"]

							# Encoder.
							for i in range(__lowerCamelCase      ):
														# Block i, layer 0 (Self Attention).
														__snake_case						:     Union[str, Any]									=  tax_layer_norm_lookup(__lowerCamelCase				,      __lowerCamelCase				,      "encoder"				,      "pre_attention_layer_norm"      )
														__snake_case					,     __snake_case					,     __snake_case					,     __snake_case						:     List[str]									=  tax_attention_lookup(__lowerCamelCase				,      __lowerCamelCase				,      "encoder"				,      "attention"      )
														__snake_case						:     List[str]									=  layer_norm
														__snake_case						:     Union[str, Any]									=  k.T
														__snake_case						:     List[str]									=  o.T
														__snake_case						:     List[str]									=  q.T
														__snake_case						:     Union[str, Any]									=  v.T

														# Block i, layer 1 (MLP).
														__snake_case						:     str									=  tax_layer_norm_lookup(__lowerCamelCase				,      __lowerCamelCase				,      "encoder"				,      "pre_mlp_layer_norm"      )
														__snake_case					,     __snake_case						:     Any									=  tax_mlp_lookup(__lowerCamelCase				,      __lowerCamelCase				,      "encoder"				,      __lowerCamelCase      )
														__snake_case						:     str									=  layer_norm
														if split_mlp_wi:
																					__snake_case						:     Optional[int]									=  wi[0].T
																					__snake_case						:     List[Any]									=  wi[1].T
														else:
																					__snake_case						:     Any									=  wi.T
														__snake_case						:     Union[str, Any]									=  wo.T
														if scalable_attention:
																					# convert the rel_embedding of each layer
																					__snake_case						:     List[Any]									=  tax_relpos_bias_lookup(
																					    __lowerCamelCase				,      __lowerCamelCase				,      "encoder"      ).T

							__snake_case						:     int									=  old["encoder/encoder_norm/scale"]

							if not scalable_attention:
														__snake_case						:     Optional[int]									=  tax_relpos_bias_lookup(
														    __lowerCamelCase				,      0				,      "encoder"      ).T
														__snake_case						:     Union[str, Any]									=  tax_relpos_bias_lookup(
														    __lowerCamelCase				,      0				,      "decoder"      ).T

							if not is_encoder_only:
														# Decoder.
														for i in range(__lowerCamelCase      ):
																					# Block i, layer 0 (Self Attention).
																					__snake_case						:     Tuple									=  tax_layer_norm_lookup(__lowerCamelCase				,      __lowerCamelCase				,      "decoder"				,      "pre_self_attention_layer_norm"      )
																					__snake_case					,     __snake_case					,     __snake_case					,     __snake_case						:     Tuple									=  tax_attention_lookup(__lowerCamelCase				,      __lowerCamelCase				,      "decoder"				,      "self_attention"      )
																					__snake_case						:     int									=  layer_norm
																					__snake_case						:     Tuple									=  k.T
																					__snake_case						:     List[Any]									=  o.T
																					__snake_case						:     str									=  q.T
																					__snake_case						:     Optional[Any]									=  v.T

																					# Block i, layer 1 (Cross Attention).
																					__snake_case						:     Optional[Any]									=  tax_layer_norm_lookup(__lowerCamelCase				,      __lowerCamelCase				,      "decoder"				,      "pre_cross_attention_layer_norm"      )
																					__snake_case					,     __snake_case					,     __snake_case					,     __snake_case						:     List[Any]									=  tax_attention_lookup(__lowerCamelCase				,      __lowerCamelCase				,      "decoder"				,      "encoder_decoder_attention"      )
																					__snake_case						:     Optional[int]									=  layer_norm
																					__snake_case						:     int									=  k.T
																					__snake_case						:     Optional[Any]									=  o.T
																					__snake_case						:     Dict									=  q.T
																					__snake_case						:     List[Any]									=  v.T

																					# Block i, layer 2 (MLP).
																					__snake_case						:     Dict									=  tax_layer_norm_lookup(__lowerCamelCase				,      __lowerCamelCase				,      "decoder"				,      "pre_mlp_layer_norm"      )
																					__snake_case					,     __snake_case						:     Dict									=  tax_mlp_lookup(__lowerCamelCase				,      __lowerCamelCase				,      "decoder"				,      __lowerCamelCase      )
																					__snake_case						:     Dict									=  layer_norm
																					if split_mlp_wi:
																												__snake_case						:     List[Any]									=  wi[0].T
																												__snake_case						:     int									=  wi[1].T
																					else:
																												__snake_case						:     Dict									=  wi.T
																					__snake_case						:     Any									=  wo.T

																					if scalable_attention:
																												# convert the rel_embedding of each layer
																												__snake_case						:     Any									=  tax_relpos_bias_lookup(__lowerCamelCase				,      __lowerCamelCase				,      "decoder"      ).T

														__snake_case						:     Dict									=  old["decoder/decoder_norm/scale"]

														# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
														if "decoder/logits_dense/kernel" in old:
																					__snake_case						:     List[Any]									=  old["decoder/logits_dense/kernel"].T

							return new







def 							lowerCAmelCase_	(   __lowerCamelCase				,      __lowerCamelCase      ):
							__snake_case						:     Optional[Any]									=  collections.OrderedDict([(k, torch.from_numpy(v.copy()      )) for (k, v) in converted_params.items()]      )

							# Add what is missing.
							if "encoder.embed_tokens.weight" not in state_dict:
														__snake_case						:     int									=  state_dict["shared.weight"]

							if not is_encoder_only:
														if "decoder.embed_tokens.weight" not in state_dict:
																					__snake_case						:     Optional[int]									=  state_dict["shared.weight"]

														if "lm_head.weight" not in state_dict:  # For old 1.0 models.
																					print("Using shared word embeddings as lm_head."      )
																					__snake_case						:     int									=  state_dict["shared.weight"]

							return state_dict







def 							lowerCAmelCase_	(   __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase      ):
							__snake_case						:     Union[str, Any]									=  checkpoints.load_tax_checkpoint(__lowerCamelCase      )
							__snake_case						:     int									=  convert_tax_to_pytorch(
							    __lowerCamelCase				,      num_layers=config.num_layers				,      is_encoder_only=__lowerCamelCase				,      scalable_attention=__lowerCamelCase      )
							__snake_case						:     List[str]									=  make_state_dict(__lowerCamelCase				,      __lowerCamelCase      )
							model.load_state_dict(__lowerCamelCase				,      strict=__lowerCamelCase      )







def 							lowerCAmelCase_	(   __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase = False				,      __lowerCamelCase = False				,      ):
							__snake_case						:     str									=  MTaConfig.from_json_file(__lowerCamelCase      )
							print(F'Building PyTorch model from configuration: {config}'      )
							# Non-v1.1 checkpoints could also use T5Model, but this works for all.
							# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
							if is_encoder_only:
														__snake_case						:     Optional[int]									=  UMTaEncoderModel(__lowerCamelCase      )
							else:
														__snake_case						:     int									=  UMTaForConditionalGeneration(__lowerCamelCase      )

							# Load weights from tf checkpoint
							load_tax_weights_in_ta(__lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase      )

							# Save pytorch-model
							print(F'Save PyTorch model to {pytorch_dump_path}'      )
							model.save_pretrained(__lowerCamelCase      )

							# Verify that we can load the checkpoint.
							model.from_pretrained(__lowerCamelCase      )
							print("Done"      )


if __name__ == "__main__":
			_snake_case	:						Optional[Any]         =							argparse.ArgumentParser(description="Converts a native T5X checkpoint into a PyTorch checkpoint.")
			# Required parameters
			parser.add_argument(
			    "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path to the T5X checkpoint."
			)
			parser.add_argument(
			    "--config_file",
			    default=None,
			    type=str,
			    required=True,
			    help="The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.",
			)
			parser.add_argument(
			    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
			)
			parser.add_argument(
			    "--is_encoder_only", action="store_true", help="Check if the model is encoder-decoder model", default=False
			)
			parser.add_argument(
			    "--scalable_attention",
			    action="store_true",
			    help="Whether the model uses scaled attention (umt5 model)",
			    default=False,
			)
			_snake_case	:						Optional[Any]         =							parser.parse_args()
			convert_tax_checkpoint_to_pytorch(
			    args.tax_checkpoint_path,
			    args.config_file,
			    args.pytorch_dump_path,
			    args.is_encoder_only,
			    args.scalable_attention,
			)


"""simple docstring"""



def 	_lowerCamelCase(	a = 1   ,    a = 1_0_0_0				):
       __a       =    1
       __a       =    0

       for divide_by_number in range(a   ,    digit + 1				):
              __a       =    []
              __a       =    numerator
              for _ in range(1   ,    digit + 1				):
                     if now_divide in has_been_divided:
                            if longest_list_length < len(a				):
                                   __a       =    len(a				)
                                   __a       =    divide_by_number
                     else:
                            has_been_divided.append(a				)
                            __a       =    now_divide * 1_0 % divide_by_number

       return the_digit


# Tests
if __name__ == "__main__":
   import doctest

   doctest.testmod()
